Try this. We first create a zoo object from the transpose of each
table using the column names as the times in the time series. Then
merge the series, transpose them back and fix up the rownames.
Finally, if needed, coerce the result to class "table".
library(zoo)
z1 <- zoo(t(t1), as.numeric(colnames(t1)))
z2 <- zoo(t(t2), as.numeric(colnames(t2)))
out <- coredata(t(merge(z1, z2)))
rownames(out) <- sub("[.].*", "", rownames(out)) # fix up rownames
out # at this point result is of class "matrix"
as.table(out) # only need this if you need result to be class "table"
or if they are in a list, L:
library(zoo)
L <- list(t1, t2)
f <- function(x) zoo(t(x), as.numeric(colnames(x)))
out <- coredata(t(do.call("merge", lapply(L, f))))
rownames(out) <- sub("[.].*", "", rownames(out)) # fix up rownames
out # at this point result is of class "matrix"
as.table(out) # only need this if you need result to be class "table"
On 5/7/06, Bernd Weiss <[EMAIL PROTECTED]> wrote:
> Suppose, I have two tables t1 and t2 which are constrcted as follows:
>
> x1 <- c(rep(0:2,10))
> x2 <- c(rep(0:1,15))
>
> y1 <- c(rep(5:10,5))
> y1 <- as.factor(y1)
>
> y2 <- c(rep(c(5,7),15))
> y2 <- as.factor(y2)
>
> t1 <- table(x1,y1)
> t2 <- table(x2,y2)
>
>
> > t1
> y1
> x1 5 6 7 8 9 10
> 0 5 0 0 5 0 0
> 1 0 5 0 0 5 0
> 2 0 0 5 0 0 5
> > t2
> y2
> x2 5 7
> 0 15 0
> 1 0 15
>
> Now, I would like to merge[1] t1 and t2 so that each missing
> categorie in t2 (compared to t1) is set to NA.
>
> 5 6 7 8 9 10
> 0 5 0 0 5 0 0
> 1 0 5 0 0 5 0
> 2 0 0 5 0 0 5
> 0 15 NA 0 NA NA NA
> 1 0 NA 15 NA NA NA
>
> Of course, this is a toy example and I need to combine t1 to t15.
>
> It is important to keep the rownames as well as the colnames.
>
> Any help is appreciated.
>
> Bernd
>
>
> [1] If I could via rbind, which, of course, is not possible,
> dim(t1)[2]!=dim(t2)[2].
>
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>
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